Notebook Overiew

This notebook is a part of a series that documents the review analysis for the DRN Cell Types project.

This notebook contains the code used to perform unsupervised clustering on the 5-HT neurons in the scRNA-seq dataset from Zeisel et al. (2018). Clusters were then filtered and identified using similar methods used in the analysis of our DRN inDrop scRNA-seq dataset.

Analysis Workflow

1. Initialize

1.1 Load libraries

library(devtools)
library(useful)
Loading required package: ggplot2
library(dplyr)

Attaching package: ‘dplyr’

The following objects are masked from ‘package:stats’:

    filter, lag

The following objects are masked from ‘package:base’:

    intersect, setdiff, setequal, union
library(ggplot2)
library(reticulate)
library(Seurat)
Loading required package: cowplot

Attaching package: ‘cowplot’

The following object is masked from ‘package:ggplot2’:

    ggsave

Loading required package: Matrix
library(stringr)
library(Matrix)
library(parallel)
library(ape)

1.2 Load data

wd <- "/Volumes/LaCie/Dropbox/Sabatini Lab/DRN Cell Types Project/DRN Cell Types Manuscript/Revisions (1)/RNA-seq/"
dRaphe.neurons <- readRDS(file.path(wd, "DRN_inDrop_neurons.rds"))
zeisel.ob <- readRDS(file.path(wd, "zeisel_Seurat_cholinergic_monoaminergic_peptidergic.rds"))
dim(dRaphe.neurons@data)
[1] 15941  2041
dim(zeisel.ob@data)
[1] 13825  3977
table(dRaphe.neurons@ident)

Peptidergic      GABA-I     GABA-II    GABA-III    GABA/Glu       Glu-I      Glu-II     Glu-III 
        103          82         132         118          32         111          79         129 
     Glu-IV       Glu-V        DA-I       DA-II      DA-III      5-HT-I     5-HT-II    5-HT-III 
         78          29          96         118         230         178         186         125 
    5-HT-IV      5-HT-V 
        208           7 
table(zeisel.ob@ident)

 DECHO1  DEINH4  DEINH5  DEINH6  DEINH7  DEINH8   HBADR  HBCHO3  HBCHO4   HBNOR  HBSER1  HBSER2 
    114      57     308      12     182     120      40      72      52      23     105     109 
 HBSER3  HBSER4  HBSER5  HYPEP1  HYPEP2  HYPEP3  HYPEP4  HYPEP5  HYPEP6  HYPEP7  HYPEP8  MBDOP1 
     87      72      64     191      43     152      98     173      32      13      15      57 
 MBDOP2 MEGLU14 MEINH14 SCINH11   TECHO  TEINH2  TEINH3 
     63      47      21      50     121    1339     145 

1.3 Check and correct gene name differences

huang.geneNames <- rownames(dRaphe.neurons@data)
zeisel.geneNames <- rownames(zeisel.ob@data)
length(huang.geneNames)
[1] 15941
length(zeisel.geneNames)
[1] 13825
length(intersect(huang.geneNames, zeisel.geneNames))
[1] 12043
zeisel.geneNames.fix <- str_replace(string = zeisel.geneNames,
                                    pattern = "-",
                                    replacement = ".")
length(intersect(huang.geneNames, zeisel.geneNames.fix))
[1] 12074
is.element("mt.Co2", huang.geneNames) & is.element("mt.Co2", zeisel.geneNames.fix)
[1] TRUE
head(setdiff(huang.geneNames, zeisel.geneNames.fix), 10)
 [1] "X.343C11.2"     "X0610007P14Rik" "X0610009B22Rik" "X0610009E02Rik" "X0610009L18Rik"
 [6] "X0610009O20Rik" "X0610010F05Rik" "X0610012G03Rik" "X0610030E20Rik" "X0610031O16Rik"
head(setdiff(zeisel.geneNames.fix, huang.geneNames), 10)
 [1] "Klf14"         "5031425F14Rik" "Esm1"          "Gm13530"       "Pmch"          "Tmprss6"      
 [7] "Acbd7"         "Gnrh1"         "A930003A15Rik" "Six6"         

Fixing them now probably matters more for trying to merge and cluster the cells from both datasets together.

We will find the intersection after separating out the 5-HT neurons from each dataset.

2. Average the Zeisel data by cluster

2.1 Subset the 5-HT neurons

zeisel.ob.SER <- SubsetData(object = zeisel.ob,
                            ident.use = c("HBSER1",
                                          "HBSER2",
                                          "HBSER3",
                                          "HBSER4",
                                          "HBSER5"),
                            subset.raw = TRUE)
dRaphe.neurons.5HT <- SubsetData(object = dRaphe.neurons,
                            ident.use = c("5-HT-I",
                                          "5-HT-II",
                                          "5-HT-III",
                                          "5-HT-IV",
                                          "5-HT-V"),
                            subset.raw = TRUE)
dim(dRaphe.neurons.5HT@data)
[1] 15941   704
dim(zeisel.ob.SER@data)
[1] 13825   437
summary(dRaphe.neurons.5HT@meta.data$nUMI)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    772    2270    4398    6068    9368   17906 
summary(zeisel.ob.SER@meta.data$nUMI)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    324    7111    9256    8775   10866   20485 
summary(dRaphe.neurons.5HT@meta.data$nGene)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    341    1130    1904    2140    3048    5110 
summary(zeisel.ob.SER@meta.data$nGene)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    290    3093    3657    3407    3993    5434 
dRaphe.neurons.5HT <- SubsetData(object = dRaphe.neurons,
                                 ident.use = c("5-HT-I",
                                               "5-HT-II",
                                               "5-HT-III",
                                               "5-HT-IV",
                                               "5-HT-V"),
                                 subset.raw = TRUE)
dim(dRaphe.neurons.5HT@data)
[1] 15941   704

2.2 Normalize the data

zeisel.ob.SER <- NormalizeData(object = zeisel.ob.SER,
                               normalization.method = "LogNormalize",
                               scale.factor = 10000,
                               display.progress = FALSE)
dRaphe.neurons.5HT <- NormalizeData(object = dRaphe.neurons.5HT,
                                    normalization.method = "LogNormalize",
                                    scale.factor = 10000,
                                    display.progress = FALSE)

2.3 Find the expressed genes in both subsets of 5-HT neurons

huang.5HT.genes <- rownames(dRaphe.neurons.5HT@raw.data[(rowSums(as.matrix(dRaphe.neurons.5HT@raw.data))>0),])
zeisel.5HT.genes <- rownames(zeisel.ob.SER@raw.data[(rowSums(as.matrix(zeisel.ob.SER@raw.data))>0),])
genes.common <- intersect(huang.5HT.genes, zeisel.5HT.genes)
length(genes.common)
[1] 11834
is.element("Tph2", genes.common)
[1] TRUE
is.element("En1", genes.common)
[1] TRUE
is.element("Fev", genes.common)
[1] TRUE
is.element("Pdyn", genes.common)
[1] TRUE
is.element("Trh", genes.common)
[1] TRUE
is.element("Cbln2", genes.common)
[1] TRUE
is.element("Met", genes.common)
[1] TRUE

2.4 Calculate cluster averages

zeisel.cluster.SER.avg <- AverageExpression(object = zeisel.ob.SER,
                                            genes.use = genes.common,
                                            return.seurat = TRUE,
                                            show.progress = FALSE)
dim(zeisel.cluster.SER.avg@data)
[1] 11834     5
huang.5HT.subtype.avg <- AverageExpression(object = dRaphe.neurons.5HT,
                                           genes.use = genes.common,
                                           return.seurat = TRUE,
                                           show.progress = FALSE)
dim(huang.5HT.subtype.avg@data)
[1] 11834     5
dRaphe.neurons <- SetIdent(object = dRaphe.neurons,
                           cells.use = WhichCells(object = dRaphe.neurons,
                                                  ident = c("5-HT-I",
                                                            "5-HT-II",
                                                            "5-HT-III",
                                                            "5-HT-IV",
                                                            "5-HT-V")),
                               ident.use = "5-HT")
dRaphe.neurons <- SetIdent(object = dRaphe.neurons,
                           cells.use = WhichCells(object = dRaphe.neurons,
                                                  ident = c("DA-I",
                                                            "DA-II",
                                                            "DA-III")),
                               ident.use = "DA")
dRaphe.neurons <- SetIdent(object = dRaphe.neurons,
                           cells.use = WhichCells(object = dRaphe.neurons,
                                                  ident = c("GABA-I",
                                                            "GABA-II",
                                                            "GABA-III")),
                               ident.use = "GABA")
dRaphe.neurons <- SetIdent(object = dRaphe.neurons,
                           cells.use = WhichCells(object = dRaphe.neurons,
                                                  ident = c("Glu-I",
                                                            "Glu-II",
                                                            "Glu-III",
                                                            "Glu-IV",
                                                            "Glu-V")),
                               ident.use = "Glu")
dRaphe.neurons.avg <- AverageExpression(object = dRaphe.neurons,
                                       genes.use = genes.common,
                                       return.seurat = TRUE,
                                       show.progress = FALSE)
dim(dRaphe.neurons.avg@data)
[1] 11834     6

Check correlation coefficients:

cor(x = as.vector(dRaphe.neurons.avg@data[,"5-HT"]), 
    y = as.vector(zeisel.cluster.SER.avg@data[,"HBSER1"]),
    method = "pearson")
[1] 0.8072475
cor(x = as.vector(dRaphe.neurons.avg@data[,"5-HT"]), 
    y = as.vector(zeisel.cluster.SER.avg@data[,"HBSER2"]),
    method = "pearson")
[1] 0.8363493
cor(x = as.vector(dRaphe.neurons.avg@data[,"5-HT"]), 
    y = as.vector(zeisel.cluster.SER.avg@data[,"HBSER3"]),
    method = "pearson")
[1] 0.7996928
cor(x = as.vector(dRaphe.neurons.avg@data[,"5-HT"]), 
    y = as.vector(zeisel.cluster.SER.avg@data[,"HBSER4"]),
    method = "pearson")
[1] 0.8160603
cor(x = as.vector(dRaphe.neurons.avg@data[,"5-HT"]), 
    y = as.vector(zeisel.cluster.SER.avg@data[,"HBSER5"]),
    method = "pearson")
[1] 0.8154013
cor(x = as.vector(zeisel.cluster.SER.avg@data[,"HBSER1"]), 
    y = as.vector(zeisel.cluster.SER.avg@data[,"HBSER2"]),
    method = "pearson")
[1] 0.9488745
cor(x = as.vector(zeisel.cluster.SER.avg@data[,"HBSER1"]), 
    y = as.vector(zeisel.cluster.SER.avg@data[,"HBSER3"]),
    method = "pearson")
[1] 0.9738681
cor(x = as.vector(zeisel.cluster.SER.avg@data[,"HBSER1"]), 
    y = as.vector(zeisel.cluster.SER.avg@data[,"HBSER4"]),
    method = "pearson")
[1] 0.9209452
cor(x = as.vector(zeisel.cluster.SER.avg@data[,"HBSER1"]), 
    y = as.vector(zeisel.cluster.SER.avg@data[,"HBSER5"]),
    method = "pearson")
[1] 0.9342197

Coefficients are all fairly high, probably because there are a lot of genes in common between all of the different 5-HT neuron clusters. Clustering the Zeisel dataset cells will probably be more informative.

3. Cluster 5-HT neurons in Zeisel dataset separately

3.1 Scale data

zeisel.ob.SER <- ScaleData(object = zeisel.ob.SER,
                           vars.to.regress = c("nUMI", "percent.mito"),
                           model.use = "linear",
                           do.scale = TRUE,
                           scale.max = 10,
                           do.center = TRUE,
                           do.par = TRUE,
                           num.cores = 4,
                           display.progress = FALSE)

3.2 Find variable genes

zeisel.ob.SER <- FindVariableGenes(object = zeisel.ob.SER,
                                   mean.function = ExpMean,
                                   dispersion.function = LogVMR,
                                   x.low.cutoff = 0.075,
                                   x.high.cutoff = 4,
                                   y.cutoff = 0.5,
                                   num.bin = 100)
Calculating gene means
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variance to mean ratios
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|

length(zeisel.ob.SER@var.genes)
[1] 1862

3.3 PCA

zeisel.ob.SER <- RunPCA(object = zeisel.ob.SER,
                        pcs.compute = 30,
                        pcs.print = NA,
                        weight.by.var = FALSE)
DimHeatmap(object = zeisel.ob.SER,
           reduction.type = "pca",
           cells.use = length(zeisel.ob.SER@cell.names),
           dim.use = 1:9,
           do.balanced = TRUE)

DimHeatmap(object = zeisel.ob.SER,
           reduction.type = "pca",
           cells.use = length(zeisel.ob.SER@cell.names),
           dim.use = 10:18,
           do.balanced = TRUE)

PCElbowPlot(object= zeisel.ob.SER, num.pc = 30)

zeisel.ob.SER <- JackStraw(object = zeisel.ob.SER,
                           num.pc = 30,
                           do.par = TRUE,
                           num.cores = 4,
                           display.progress = FALSE)
JackStrawPlot(object = zeisel.ob.SER,
              PCs = 1:30)

3.4 DR (UMAP)

zeisel.ob.SER <- RunUMAP(object = zeisel.ob.SER,
                         reduction.use = "pca",
                         dims.use = 1:10,
                         n_neighbors = 30L,
                         min_dist = 0.5,
                         metric = "correlation")
DimPlot(object = zeisel.ob.SER,
        reduction.use = "umap",
        no.legend = TRUE,
        do.label = TRUE,
        pt.size = 3,
        label.size = 10)

3.5 Check marker expression

VlnPlot(object = zeisel.ob.SER,
        features.plot = c("Slc6a4",
                          "Tph2",
                          "Fev",
                          "Htr1a",
                          "Slc22a3",
                          "Maob"),
        nCol = 1,
        point.size.use = 0,
        size.x.use = 5,
        size.y.use = 5,
        x.lab.rot = TRUE)

VlnPlot(object = zeisel.ob.SER,
        features.plot = c("En1",
                          "En2",
                          "Pou3f1",
                          "Met",
                          "Plxnb1",
                          "Pnoc",
                          "Htr2a"),
        nCol = 1,
        point.size.use = 0,
        size.x.use = 5,
        size.y.use = 5,
        x.lab.rot = TRUE)

The R2-enriched gene Hoxa2 was not detected.

FeaturePlot(object = zeisel.ob.SER,
            reduction.use = "umap",
            features.plot = c("nUMI", "Snap25", "En1",
                              "Gabre", "Sema5a", "Kit",
                              "Hrh3", "Slc17a8", "Arhgap36"),
            cols.use = c("gray", "red"))

FeaturePlot(object = zeisel.ob.SER,
            reduction.use = "umap",
            features.plot = c("Slc6a4", "Tph2", "Fev",
                              "En1", "En2", "Pnoc",
                              "Slc17a8", "Pdyn", "Prkcq"),
            cols.use = c("gray", "red"))

FeaturePlot(object = zeisel.ob.SER,
            reduction.use = "umap",
            features.plot = c("Trh", "Asb4", "Cbln2",
                              "Gad1", "Gad2", "Tac1",
                              "Nr2f2", "Cdh4", "Oxtr"),
            cols.use = c("gray", "red"))

3.6 Clustering

resolution.vector <- c(0.5, 0.6, 0.8, 1, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8)
nClusters <- rep(0, length(resolution.vector))
oobe.max <- rep(0, length(resolution.vector))
oobe.min <- rep(0, length(resolution.vector))
oobe.mean <- rep(0, length(resolution.vector))
for (i in 1:length(resolution.vector)) {
  zeisel.ob.SER <- FindClusters(object = zeisel.ob.SER,
                              reduction.type = "pca",
                              dims.use = 1:10,
                              resolution = resolution.vector[i],
                              algorithm = 3,
                              n.iter = 10,
                              save.SNN = TRUE,
                              force.recalc = TRUE,
                              print.output = FALSE)
  
  # Average expression and build cluster tree
  zeisel.ob.SER.averages <- AverageExpression(object = zeisel.ob.SER, 
                                               return.seurat = TRUE,
                                               show.progress = FALSE)
  d <- dist(x = t(zeisel.ob.SER.averages@scale.data), 
            method = "euclidean")
  hc <- hclust(d, method = "ward.D2")
  oldIDs <- hc$labels[hc$order]
  newIDs <- c(1:length(levels(zeisel.ob.SER@ident)))
  zeisel.ob.SER@ident <- plyr::mapvalues(x = zeisel.ob.SER@ident,
                                          from = oldIDs,
                                          to = newIDs)
  level.order <- order(as.numeric(levels(zeisel.ob.SER@ident)))
  zeisel.ob.SER@ident <- factor(x = zeisel.ob.SER@ident,
                                 levels = levels(zeisel.ob.SER@ident)[level.order],
                                 ordered = TRUE)
  # Rebuild and store the cluster tree on the new identities
  zeisel.ob.SER.averages <- AverageExpression(object = zeisel.ob.SER, 
                                               return.seurat = TRUE,
                                               show.progress = FALSE)
  d <- dist(x = t(zeisel.ob.SER.averages@scale.data), 
            method = "euclidean")
  hc <- hclust(d, method = "ward.D2")
  zeisel.ob.SER@cluster.tree[[1]] <- as.phylo(hc)
  nodes.oobe <- AssessNodes(object = zeisel.ob.SER, 
                            genes.training = zeisel.ob.SER@var.genes)
  
  nClusters[i] <- length(levels(zeisel.ob.SER@ident))
  oobe.max[i] <- max(as.vector(nodes.oobe$oobe))
  oobe.min[i] <- min(as.vector(nodes.oobe$oobe))
  oobe.mean[i] <- mean(as.vector(nodes.oobe$oobe))
}

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clusteringResults <- data.frame(resolution.vector,
                                nClusters,
                                oobe.min,
                                oobe.max,
                                oobe.mean)
plot.nClusters <- ggplot(data = clusteringResults,
                         aes(x = resolution.vector,
                             y = nClusters)) + geom_line() + geom_point()
plot.oobe.min <- ggplot(data = clusteringResults,
                         aes(x = resolution.vector,
                             y = oobe.min)) + geom_line() + geom_point()
plot.oobe.max <- ggplot(data = clusteringResults,
                         aes(x = resolution.vector,
                             y = oobe.max)) + geom_line() + geom_point()
plot.oobe.mean <- ggplot(data = clusteringResults,
                         aes(x = resolution.vector,
                             y = oobe.mean)) + geom_line() + geom_point()
plot.list <- list(plot.nClusters,
                  plot.oobe.min,
                  plot.oobe.max,
                  plot.oobe.mean)
plot_grid(plotlist = plot.list, ncol = 2)

zeisel.ob.SER <- FindClusters(object = zeisel.ob.SER,
                              reduction.type = "pca",
                              dims.use = 1:10,
                              resolution = 1.5,
                              algorithm = 3,
                              n.iter = 10,
                              save.SNN = TRUE,
                              force.recalc = TRUE,
                              print.output = FALSE)
# Average expression and build cluster tree
zeisel.ob.SER.averages <- AverageExpression(object = zeisel.ob.SER, 
                                             return.seurat = TRUE,
                                             show.progress = FALSE)
d <- dist(x = t(zeisel.ob.SER.averages@scale.data), 
          method = "euclidean")
hc <- hclust(d, method = "ward.D2")
oldIDs <- hc$labels[hc$order]
newIDs <- c(1:length(levels(zeisel.ob.SER@ident)))
zeisel.ob.SER@ident <- plyr::mapvalues(x = zeisel.ob.SER@ident,
                                        from = oldIDs,
                                        to = newIDs)
level.order <- order(as.numeric(levels(zeisel.ob.SER@ident)))
zeisel.ob.SER@ident <- factor(x = zeisel.ob.SER@ident,
                               levels = levels(zeisel.ob.SER@ident)[level.order],
                               ordered = TRUE)
# Rebuild and store the cluster tree on the new identities
zeisel.ob.SER.averages <- AverageExpression(object = zeisel.ob.SER, 
                                             return.seurat = TRUE,
                                             show.progress = FALSE)
d <- dist(x = t(zeisel.ob.SER.averages@scale.data), 
          method = "euclidean")
hc <- hclust(d, method = "ward.D2")
zeisel.ob.SER@cluster.tree[[1]] <- as.phylo(hc)
PlotClusterTree(zeisel.ob.SER)

p1 <- DimPlot(object = zeisel.ob.SER,
              reduction.use = "umap",
              no.legend = TRUE,
              do.label = TRUE,
              pt.size = 3,
              label.size = 10,
              do.return = TRUE)
p2 <- DimPlot(object = zeisel.ob.SER,
              reduction.use = "umap",
              no.legend = TRUE,
              do.label = TRUE,
              pt.size = 3,
              label.size = 10,
              group.by = "zeisel.clusterNames",
              do.return = TRUE)
plot_grid(p1, p2)

nodes.oobe <- AssessNodes(zeisel.ob.SER, genes.training = zeisel.ob.SER@var.genes)

   |                                                  | 0 % ~calculating  
   |++++++++                                          | 14% ~08s          
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   |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 07s
nodes.oobe <- nodes.oobe[order(nodes.oobe$oobe, decreasing = TRUE),]
nodes.oobe
nodes.oobe
VlnPlot(object = zeisel.ob.SER,
        features.plot = c("nUMI",
                          "Slc6a4",
                          "Tph2",
                          "Fev",
                          "Htr1a",
                          "Slc22a3",
                          "Maob"),
        nCol = 1,
        point.size.use = 0,
        size.x.use = 5,
        size.y.use = 5,
        x.lab.rot = TRUE)

VlnPlot(object = zeisel.ob.SER,
        features.plot = c("En1",
                          "En2",
                          "Pou3f1",
                          "Met",
                          "Plxnb1",
                          "Pnoc",
                          "Htr2a"),
        nCol = 1,
        point.size.use = 0,
        size.x.use = 5,
        size.y.use = 5,
        x.lab.rot = TRUE)

table(zeisel.ob.SER@ident)

  1   2   3   4   5   6   7   8 
 38  67  50  20  25  58 103  76 

3.7 Find genes enriched in each cluster

markers.cluster <- FindAllMarkers(object = zeisel.ob.SER,
                                  test.use = "MAST",
                                  only.pos = TRUE)
..............................................................
Done!
Combining coefficients and standard errors
Calculating log-fold changes
Calculating likelihood ratio tests
Refitting on reduced model...
..............................................................
Done!
................................................
Done!
Combining coefficients and standard errors
Calculating log-fold changes
Calculating likelihood ratio tests
Refitting on reduced model...
................................................
Done!
.........................................
Done!
Combining coefficients and standard errors
Calculating log-fold changes
Calculating likelihood ratio tests
Refitting on reduced model...
.........................................
Done!
............................................................
Done!
Combining coefficients and standard errors
Calculating log-fold changes
Calculating likelihood ratio tests
Refitting on reduced model...
............................................................
Done!
..................................................
Done!
Combining coefficients and standard errors
Calculating log-fold changes
Calculating likelihood ratio tests
Refitting on reduced model...
..................................................
Done!
..............................
Done!
Combining coefficients and standard errors
Calculating log-fold changes
Calculating likelihood ratio tests
Refitting on reduced model...
..............................
Done!
................................
Done!
Combining coefficients and standard errors
Calculating log-fold changes
Calculating likelihood ratio tests
Refitting on reduced model...
................................
Done!
................
Done!
Combining coefficients and standard errors
Calculating log-fold changes
Calculating likelihood ratio tests
Refitting on reduced model...
................
Done!
markers.node <- FindAllMarkersNode(object = zeisel.ob.SER,
                                   test.use = "MAST",
                                   only.pos = FALSE)
............................................
Done!
Combining coefficients and standard errors
Calculating log-fold changes
Calculating likelihood ratio tests
Refitting on reduced model...
............................................
Done!
....................................
Done!
Combining coefficients and standard errors
Calculating log-fold changes
Calculating likelihood ratio tests
Refitting on reduced model...
....................................
Done!
................................................
Done!
Combining coefficients and standard errors
Calculating log-fold changes
Calculating likelihood ratio tests
Refitting on reduced model...
................................................
Done!
...........................................
Done!
Combining coefficients and standard errors
Calculating log-fold changes
Calculating likelihood ratio tests
Refitting on reduced model...
...........................................
Done!
......................
Done!
Combining coefficients and standard errors
Calculating log-fold changes
Calculating likelihood ratio tests
Refitting on reduced model...
......................
Done!
..........................
Done!
Combining coefficients and standard errors
Calculating log-fold changes
Calculating likelihood ratio tests
Refitting on reduced model...
..........................
Done!
....................
Done!
Combining coefficients and standard errors
Calculating log-fold changes
Calculating likelihood ratio tests
Refitting on reduced model...
....................
Done!
head(markers.cluster[markers.cluster$cluster==1,], 100)

Cluster 1 is highly enriched in ribosomal genes, and the separation seems likely to be an artifact. Should also check DE genes at node #10.

head(markers.cluster[markers.cluster$cluster==2,], 50)

Cluster 2 is likely a part of R1DR (dorsal DRN).

head(markers.cluster[markers.cluster$cluster==3,], 50)

Cluster 3 is likely a part of R1DR (ventral DRN).

head(markers.cluster[markers.cluster$cluster==4,], 50)
FeaturePlot(object = zeisel.ob.SER,
            reduction.use = "umap",
            features.plot = c("Gpr101", "Trh", "Sst",
                              "Penk", "Trhr", "Reln",
                              "Ndnf", "Mc4r", "Pou3f2"),
            cols.use = c("gray", "red"))

Cluster 4 gene expression matches the R6P cluster in Okaty et al. (2015).

head(markers.cluster[markers.cluster$cluster==5,], 50)
FeaturePlot(object = zeisel.ob.SER,
            reduction.use = "umap",
            features.plot = c("Mal", "Cryab", "Fa2h",
                              "Kcnj10", "Enpp2", "Olig1",
                              "Mog", "Kcna1", "Nkx6-2"),
            cols.use = c("gray", "red"))

Cells in cluster 5 look a lot like putative doublets, given the high expression of genes typically found in glial cells (particularly oligodendrocyte genes).

head(markers.cluster[markers.cluster$cluster==6,], 50)

Cluster 6 is likely to be R1MR (or R1DR/R2).

head(markers.cluster[markers.cluster$cluster==7,], 50)

Cluster 7 cells are likely to be R5 cells.

FeaturePlot(object = zeisel.ob.SER,
            reduction.use = "umap",
            features.plot = c("Pgr15l", "Tac1", "Shox2",
                              "Chodl", "Nkx6-1", "Meis2",
                              "Islr2", "Arhgap36", "Trh"),
            cols.use = c("gray", "red"))

head(markers.cluster[markers.cluster$cluster==8,], 50)
FeaturePlot(object = zeisel.ob.SER,
            reduction.use = "umap",
            features.plot = c("Rorb", "Pcdh19", "Nr2f2",
                              "Tcf4", "Foxp1", "Esr2",
                              "Kit", "Sema5a", "Cntn3"),
            cols.use = c("gray", "red"))

Cluster 8 is either R1MR or R2, likely R2 (MRN cells, either way).

Check expression of R3-enriched genes:

FeaturePlot(object = zeisel.ob.SER,
            reduction.use = "umap",
            features.plot = c("Egr2", "Samd9l", "Plxnb1",
                              "Pdlim5", "Pcdh18", "Zfp560",
                              "Trpc5", "Sstr1", "Cdh9"),
            cols.use = c("gray", "red"))
All cells have the same value of Egr2.All cells have the same value of Trpc5.

It’s possible that there aren’t enough R3 cells in this dataset to detect.

Check DRN 5-HT subtypes:

FeaturePlot(object = zeisel.ob.SER,
            reduction.use = "umap",
            features.plot = c("En1", "Adcy2", "Pdyn",
                              "Slc17a8", "Cbln2", "Arhgap36",
                              "Prkcq", "Gad1", "Trh"),
            cols.use = c("gray", "red"))

FeaturePlot(object = zeisel.ob.SER,
            reduction.use = "umap",
            features.plot = c("Hcrtr1", "Fbxo2", "Sorcs1",
                              "Crhr2", "Rprm", "Asb4",
                              "En2", "Trpc3", "Met"),
            cols.use = c("gray", "red"))

3.8 Check DE genes by nodes (left vs. right)

head(markers.node[markers.node$cluster==9,], 50)
head(markers.node[markers.node$cluster==10,], 50)
FeaturePlot(object = zeisel.ob.SER,
            reduction.use = "umap",
            features.plot = c("Gabre", "Sema5a", "Gabrq",
                              "Tac1", "Pou2f2", "Hrh3",
                              "En1", "Amigo2", "Kit"),
            cols.use = c("gray", "red"))

Cluster 1 seems to be a collection of cells from different clusters (mixed expression of genes in R1/R2/R3/R5/R6 cells) that share “high expression”" on ribosomal genes.

head(markers.node[markers.node$cluster==14,], 50)
FeaturePlot(object = zeisel.ob.SER,
            reduction.use = "umap",
            features.plot = c("Slc17a8", "Slit2", "Pcdh11x",
                              "Nos1", "Npy2r", "Crhr2",
                              "Pcdh15", "Maf", "Ntng1"),
            cols.use = c("gray", "red"))

FeaturePlot(object = zeisel.ob.SER,
            reduction.use = "umap",
            features.plot = c("Hs3st4", "Asb4", "Prph",
                              "Arhgap36", "Gad1", "Tmem176b",
                              "Sdk2", "Hcrtr1", "Esm1"),
            cols.use = c("gray", "red"))

Node 14 is probably a split between dorsal and ventral DRN.

head(markers.node[markers.node$cluster==11,], 50)
head(markers.node[markers.node$cluster==12,], 50)
head(markers.node[markers.node$cluster==13,], 50)
head(markers.node[markers.node$cluster==15,], 50)

3.9 Filter clusters and re-cluster

zeisel.ob.SER <- SubsetData(object = zeisel.ob.SER,
                            ident.remove = c(1, 5),
                            subset.raw = TRUE)
resolution.vector <- c(0.5, 0.6, 0.8, 1, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8)
nClusters <- rep(0, length(resolution.vector))
oobe.max <- rep(0, length(resolution.vector))
oobe.min <- rep(0, length(resolution.vector))
oobe.mean <- rep(0, length(resolution.vector))
for (i in 1:length(resolution.vector)) {
  zeisel.ob.SER <- FindClusters(object = zeisel.ob.SER,
                              reduction.type = "pca",
                              dims.use = 1:10,
                              resolution = resolution.vector[i],
                              algorithm = 3,
                              n.iter = 10,
                              save.SNN = TRUE,
                              force.recalc = TRUE,
                              print.output = FALSE)
  
  # Average expression and build cluster tree
  zeisel.ob.SER.averages <- AverageExpression(object = zeisel.ob.SER, 
                                               return.seurat = TRUE,
                                               show.progress = FALSE)
  d <- dist(x = t(zeisel.ob.SER.averages@scale.data), 
            method = "euclidean")
  hc <- hclust(d, method = "ward.D2")
  oldIDs <- hc$labels[hc$order]
  newIDs <- c(1:length(levels(zeisel.ob.SER@ident)))
  zeisel.ob.SER@ident <- plyr::mapvalues(x = zeisel.ob.SER@ident,
                                          from = oldIDs,
                                          to = newIDs)
  level.order <- order(as.numeric(levels(zeisel.ob.SER@ident)))
  zeisel.ob.SER@ident <- factor(x = zeisel.ob.SER@ident,
                                 levels = levels(zeisel.ob.SER@ident)[level.order],
                                 ordered = TRUE)
  # Rebuild and store the cluster tree on the new identities
  zeisel.ob.SER.averages <- AverageExpression(object = zeisel.ob.SER, 
                                               return.seurat = TRUE,
                                               show.progress = FALSE)
  d <- dist(x = t(zeisel.ob.SER.averages@scale.data), 
            method = "euclidean")
  hc <- hclust(d, method = "ward.D2")
  zeisel.ob.SER@cluster.tree[[1]] <- as.phylo(hc)
  nodes.oobe <- AssessNodes(object = zeisel.ob.SER, 
                            genes.training = zeisel.ob.SER@var.genes)
  
  nClusters[i] <- length(levels(zeisel.ob.SER@ident))
  oobe.max[i] <- max(as.vector(nodes.oobe$oobe))
  oobe.min[i] <- min(as.vector(nodes.oobe$oobe))
  oobe.mean[i] <- mean(as.vector(nodes.oobe$oobe))
}

   |                                                  | 0 % ~calculating  
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   |                                                  | 0 % ~calculating  
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   |                                                  | 0 % ~calculating  
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   |                                                  | 0 % ~calculating  
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clusteringResults <- data.frame(resolution.vector,
                                nClusters,
                                oobe.min,
                                oobe.max,
                                oobe.mean)
plot.nClusters <- ggplot(data = clusteringResults,
                         aes(x = resolution.vector,
                             y = nClusters)) + geom_line() + geom_point()
plot.oobe.min <- ggplot(data = clusteringResults,
                         aes(x = resolution.vector,
                             y = oobe.min)) + geom_line() + geom_point()
plot.oobe.max <- ggplot(data = clusteringResults,
                         aes(x = resolution.vector,
                             y = oobe.max)) + geom_line() + geom_point()
plot.oobe.mean <- ggplot(data = clusteringResults,
                         aes(x = resolution.vector,
                             y = oobe.mean)) + geom_line() + geom_point()
plot.list <- list(plot.nClusters,
                  plot.oobe.min,
                  plot.oobe.max,
                  plot.oobe.mean)
plot_grid(plotlist = plot.list, ncol = 2)

zeisel.ob.SER <- FindClusters(object = zeisel.ob.SER,
                              reduction.type = "pca",
                              dims.use = 1:10,
                              resolution = 1.1,
                              algorithm = 3,
                              n.iter = 10,
                              save.SNN = TRUE,
                              force.recalc = TRUE,
                              print.output = FALSE)
# Average expression and build cluster tree
zeisel.ob.SER.averages <- AverageExpression(object = zeisel.ob.SER, 
                                             return.seurat = TRUE,
                                             show.progress = FALSE)
d <- dist(x = t(zeisel.ob.SER.averages@scale.data), 
          method = "euclidean")
hc <- hclust(d, method = "ward.D2")
oldIDs <- hc$labels[hc$order]
newIDs <- c(1:length(levels(zeisel.ob.SER@ident)))
zeisel.ob.SER@ident <- plyr::mapvalues(x = zeisel.ob.SER@ident,
                                        from = oldIDs,
                                        to = newIDs)
level.order <- order(as.numeric(levels(zeisel.ob.SER@ident)))
zeisel.ob.SER@ident <- factor(x = zeisel.ob.SER@ident,
                               levels = levels(zeisel.ob.SER@ident)[level.order],
                               ordered = TRUE)
# Rebuild and store the cluster tree on the new identities
zeisel.ob.SER.averages <- AverageExpression(object = zeisel.ob.SER, 
                                             return.seurat = TRUE,
                                             show.progress = FALSE)
d <- dist(x = t(zeisel.ob.SER.averages@scale.data), 
          method = "euclidean")
hc <- hclust(d, method = "ward.D2")
zeisel.ob.SER@cluster.tree[[1]] <- as.phylo(hc)
PlotClusterTree(zeisel.ob.SER)

zeisel.ob.SER <- RunUMAP(object = zeisel.ob.SER,
                         reduction.use = "pca",
                         dims.use = 1:10,
                         n_neighbors = 30L,
                         min_dist = 0.5,
                         metric = "correlation")
p1 <- DimPlot(object = zeisel.ob.SER,
              reduction.use = "umap",
              no.legend = TRUE,
              do.label = TRUE,
              pt.size = 3,
              label.size = 10,
              do.return = TRUE)
p2 <- DimPlot(object = zeisel.ob.SER,
              reduction.use = "umap",
              no.legend = TRUE,
              do.label = TRUE,
              pt.size = 3,
              label.size = 10,
              group.by = "zeisel.clusterNames",
              do.return = TRUE)
plot_grid(p1, p2)

p1

# QC, 5-HT markers
FeaturePlot(object = zeisel.ob.SER,
            reduction.use = "umap",
            features.plot = c("nUMI", "percent.mito", "Ddc",
                              "Slc18a2", "Tph2", "Slc6a4",
                              "Maob", "Slc22a3", "Qdpr"),
            cols.use = c("gray", "red"))

# Zeisel et al. (2018), HBSER4 vs. HBSER5
FeaturePlot(object = zeisel.ob.SER,
            reduction.use = "umap",
            features.plot = c("Lat2", "Gpr101", "Penk",
                              "Trh", "Sst", "Calcr",
                              "Shox2", "Chodl", "Prkcq"),
            cols.use = c("gray", "red"))

# Zeisel et al. (2018), HBSER1 vs. HBSER3
FeaturePlot(object = zeisel.ob.SER,
            reduction.use = "umap",
            features.plot = c("Satb2", "Nbl1", "Satb2",
                              "Lrrk1", "Wfdc12", "Kit",
                              "Kcns1", "Col25a1", "Il1r1"),
            cols.use = c("gray", "red"))

# Zeisel et al. (2018), HBSER2
FeaturePlot(object = zeisel.ob.SER,
            reduction.use = "umap",
            features.plot = c("Sostdc1", "Rasgrp1", "Gata3",
                              "Htr5b", "Sox1", "Pcsk5",
                              "Hs6st2", "Mbp", "Nos1"),
            cols.use = c("gray", "red"))

# R1-DR
FeaturePlot(object = zeisel.ob.SER,
            reduction.use = "umap",
            features.plot = c("En1", "En2", "Satb2",
                              "Npy2r", "Pax5", "Rspo3",
                              "Foxa1", "Pcsk6", "Prph"),
            cols.use = c("gray", "red"))

# R1-DR, dorsal/lateral vs. ventral/midline
FeaturePlot(object = zeisel.ob.SER,
            reduction.use = "umap",
            features.plot = c("Asb4", "Gad1", "Prkcq",
                              "Trh", "Hcrtr1", "Pdyn",
                              "Rprm", "Slc17a8", "Cbln2"),
            cols.use = c("gray", "red"))

# R1-MR
FeaturePlot(object = zeisel.ob.SER,
            reduction.use = "umap",
            features.plot = c("Sox1", "Rorb", "Chrm2",
                              "Trpc5", "Esr2", "Epha4",
                              "Ppp1r17", "Pkd2l1", "Col19a1"),
            cols.use = c("gray", "red"))
All cells have the same value of Trpc5.

# R2
FeaturePlot(object = zeisel.ob.SER,
            reduction.use = "umap",
            features.plot = c("Rorb", "Pcdh19", "Nr2f2",
                              "Tcf4", "Foxp1", "Met",
                              "Kit", "Sema5a", "Cntn3"),
            cols.use = c("gray", "red"))

#R5
FeaturePlot(object = zeisel.ob.SER,
            reduction.use = "umap",
            features.plot = c("Chodl", "Shox2", "Meis2",
                              "Tac1", "Nkx6-1", "Grm4",
                              "Pnoc", "Perp", "Islr2"),
            cols.use = c("gray", "red"))

# R6P
FeaturePlot(object = zeisel.ob.SER,
            reduction.use = "umap",
            features.plot = c("Gpr101", "Sst", "Penk",
                              "Trhr", "Reln", "Ndnf",
                              "Mc4r", "Pou3f2", "Trh"),
            cols.use = c("gray", "red"))

There appear to be additional subsets within clusters 2 and 3. We will explore this later, and go with the larger grouping for now and assign cluster identities by rhombomeres.

nodes.oobe <- AssessNodes(zeisel.ob.SER, genes.training = zeisel.ob.SER@var.genes)

   |                                                  | 0 % ~calculating  
   |++++++++++                                        | 20% ~08s          
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   |++++++++++++++++++++++++++++++++++++++++         | 80% ~02s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 08s
nodes.oobe <- nodes.oobe[order(nodes.oobe$oobe, decreasing = TRUE),]
nodes.oobe

Find genes enriched in each cluster

markers.cluster <- FindAllMarkers(object = zeisel.ob.SER,
                                  test.use = "MAST",
                                  only.pos = TRUE)
...........................................................
Done!
Combining coefficients and standard errors
Calculating log-fold changes
Calculating likelihood ratio tests
Refitting on reduced model...
...........................................................
Done!
............................
Done!
Combining coefficients and standard errors
Calculating log-fold changes
Calculating likelihood ratio tests
Refitting on reduced model...
............................
Done!
...............................
Done!
Combining coefficients and standard errors
Calculating log-fold changes
Calculating likelihood ratio tests
Refitting on reduced model...
...............................
Done!
...............
Done!
Combining coefficients and standard errors
Calculating log-fold changes
Calculating likelihood ratio tests
Refitting on reduced model...
...............
Done!
.......................................................
Done!
Combining coefficients and standard errors
Calculating log-fold changes
Calculating likelihood ratio tests
Refitting on reduced model...
.......................................................
Done!
.............................................
Done!
Combining coefficients and standard errors
Calculating log-fold changes
Calculating likelihood ratio tests
Refitting on reduced model...
.............................................
Done!
markers.node <- FindAllMarkersNode(object = zeisel.ob.SER,
                                   test.use = "MAST",
                                   only.pos = FALSE)
......................................................
Done!
Combining coefficients and standard errors
Calculating log-fold changes
Calculating likelihood ratio tests
Refitting on reduced model...
......................................................
Done!
................................................
Done!
Combining coefficients and standard errors
Calculating log-fold changes
Calculating likelihood ratio tests
Refitting on reduced model...
................................................
Done!
......................
Done!
Combining coefficients and standard errors
Calculating log-fold changes
Calculating likelihood ratio tests
Refitting on reduced model...
......................
Done!
...........................
Done!
Combining coefficients and standard errors
Calculating log-fold changes
Calculating likelihood ratio tests
Refitting on reduced model...
...........................
Done!
....................
Done!
Combining coefficients and standard errors
Calculating log-fold changes
Calculating likelihood ratio tests
Refitting on reduced model...
....................
Done!
head(markers.cluster[markers.cluster$cluster==1,], 50)
head(markers.cluster[markers.cluster$cluster==2,], 50)
head(markers.cluster[markers.cluster$cluster==3,], 50)
head(markers.cluster[markers.cluster$cluster==4,], 50)
head(markers.cluster[markers.cluster$cluster==5,], 50)
head(markers.cluster[markers.cluster$cluster==6,], 50)
head(markers.node[markers.node$cluster==7,], 50)
head(markers.node[markers.node$cluster==8,], 50)
head(markers.node[markers.node$cluster==9,], 50)
head(markers.node[markers.node$cluster==10,], 50)
head(markers.node[markers.node$cluster==11,], 50)

Could not find any cells/clusters corresponding to R3 in this dataset.

Cluster 2 looks very similar to clusters 5 and 6. Compare cluster 2 vs. 5 & 6:

markers.compare <- FindMarkers(object = zeisel.ob.SER,
                               ident.1 = 2,
                               ident.2 = c(5, 6),
                               test.use = "MAST",
                               only.pos = FALSE)
................................................................
Done!
Combining coefficients and standard errors
Calculating log-fold changes
Calculating likelihood ratio tests
Refitting on reduced model...
................................................................
Done!
head(markers.compare, 50)
FeaturePlot(object = zeisel.ob.SER,
            reduction.use = "umap",
            features.plot = c("Eef1a1", "Rpl41", "Rps19",
                              "Rpl23a", "Rpl37a", "Rps28",
                              "Tmsb10", "mt-Co1", "Rps29"),
            cols.use = c("gray", "red"))

FeaturePlot(object = zeisel.ob.SER,
            reduction.use = "umap",
            features.plot = c("Cldn11", "Ptges3", "Elmod1",
                              "Gprasp2", "St13", "Nptn",
                              "Gmps", "Xist", "Klhl9"),
            cols.use = c("gray", "red"))

Check other R1-MR vs. R1-DR genes:

FeaturePlot(object = zeisel.ob.SER,
            reduction.use = "umap",
            features.plot = c("Npy1r", "Sox14", "Epha4",
                              "Col14a1", "Robo1", "Ret",
                              "Sema3e", "Cxcl14", "Klhl14"),
            cols.use = c("gray", "red"))

VlnPlot(object = zeisel.ob.SER,
        features.plot = c("nUMI",
                          "nGene",
                          "percent.mito",
                          "Xist",
                          "Tph2"),
        nCol = 1,
        point.size.use = 0,
        size.x.use = 12,
        size.y.use = 5,
        x.lab.rot = FALSE)

Clusters 5 and 6 appear to be similar to cluster 2, with the separation being driven by differences that are likely to be technical or batch effects. However, there is insufficient metadata to examine this or to try and regress out these effects. It is possible that the HBSER1 cluster is separated from the other 5-HT neuron clusters in the Zeisel et al. (2018) dataset due to the same technical artifacts.

Cluster 2 appears to be a mixture of various R1 neurons (mostly R1-DR), while cluster 4 contains several MRN 5-HT neuron types that include both R1-MR and R2 neurons. It is unclear if there are any R3 neurons in this cluster, since we cannot detect cells expressing R3-enriched genes.

table(zeisel.ob.SER@ident)

  1   2   3   4   5   6 
 20  57 103  76  68  50 
zeisel.ob.SER <- StashIdent(object = zeisel.ob.SER,
                            save.name = "clusterNumbers_filtered")

3.10 Re-name clusters

ids.current <- levels(zeisel.ob.SER@ident)
ids.rhombomere <- c("R6P (ROb/RPa)",  #1
                    "R1 (DRN)",  #2 -- probably R1-DR
                    "R5 (RMg)",  #3
                    "R1/R2 (MRN)",  #4 -- at least R1-MR and R2
                    "R1 (lateral DRN)",  #5
                    "R1 (medial DRN)")  #6
zeisel.ob.SER@ident <- plyr::mapvalues(x = zeisel.ob.SER@ident,
                                        from = ids.current,
                                        to = ids.rhombomere)
zeisel.ob.SER <- StashIdent(object = zeisel.ob.SER,
                            save.name = "clusterNames_byRhombomere")
p1 <- DimPlot(object = zeisel.ob.SER,
              reduction.use = "umap",
              no.legend = TRUE,
              do.label = TRUE,
              pt.size = 3,
              label.size = 6,
              do.return = TRUE)
p2 <- DimPlot(object = zeisel.ob.SER,
              reduction.use = "umap",
              no.legend = TRUE,
              do.label = TRUE,
              pt.size = 3,
              label.size = 6,
              group.by = "zeisel.clusterNames",
              do.return = TRUE)
plot_grid(p1, p2)

table(zeisel.ob.SER@ident)

   R6P (ROb/RPa)         R1 (DRN)         R5 (RMg)      R1/R2 (MRN) R1 (lateral DRN)  R1 (medial DRN) 
              20               57              103               76               68               50 

4. Save the object with current labels

saveRDS(object = zeisel.ob.SER,
        file = "/Volumes/LaCie/Dropbox/Sabatini Lab/DRN Cell Types Project/DRN Cell Types Manuscript/Revisions (1)/RNA-seq/zeisel_Seurat_HBSER_clustered_filtered.rds")

5. Check distribution of MitoRiboRatio

library(loomR)
Loading required package: R6
Loading required package: hdf5r

Attaching package: ‘loomR’

The following object is masked from ‘package:dplyr’:

    combine

The following object is masked from ‘package:devtools’:

    create
lfile <- connect(filename = "/Volumes/LaCie/Dropbox/Sabatini Lab/DRN Cell Types Project/DRN Cell Types Manuscript/Revisions (1)/RNA-seq/l6_r3_cholinergic_monoaminergic_and_peptidergic_neurons.loom")
lfile
Class: loom
Filename: /Volumes/LaCie/Dropbox/Sabatini Lab/DRN Cell Types Project/DRN Cell Types Manuscript/Revisions (1)/RNA-seq/l6_r3_cholinergic_monoaminergic_and_peptidergic_neurons.loom
Access type: H5F_ACC_RDONLY
Attributes: CreationDate, last_modified
Listing:
       name    obj_type dataset.dims dataset.type_class
  col_attrs   H5I_GROUP         <NA>               <NA>
 col_graphs   H5I_GROUP         <NA>               <NA>
     layers   H5I_GROUP         <NA>               <NA>
     matrix H5I_DATASET 3977 x 27998          H5T_FLOAT
  row_attrs   H5I_GROUP         <NA>               <NA>
 row_graphs   H5I_GROUP         <NA>               <NA>
hist(log2(lfile$col.attrs$MitoRiboRatio[]+1), breaks = 100)

Looks similar to the distribution of percent.mito. Might be worth adding this in and trying to include it as a latent variable to see if we can remove the effects driving the separation of cluster 2 from 5 and 6.

lfile$close_all()

Summary & Conclusions

The dataset from Zeisel et al. (2018) might not serve as a sutiable “reference” dataset for comparing with our inDrop scRNA-seq dataset for the following reasons:

  • 5-HT neurons in the Zeisel dataset come from multiple serotonergic nuclei, not only the dorsal raphe.

  • The clustering of 5-HT neurons by Zeisel et al. does not match the 5-HT neuron subsets described in Okaty et al. (2015), and re-clustering on the 5-HT neurons alone shows that the HBSER1-5 clusters are likely to be a mixture of 5-HT neuron types when categorized by their developmental lineage (also explaining the unusual list of DE genes in the Zeisel et al. data portal).

  • There appear to be several sources of technical variation or batch effects that drive the separation of clusters that are most similar to R1-DR cells, but there is insufficient metadata to understand the sources of these artifacts.

  • There are only 118 DRN 5-HT neurons (R1-DR) remaining if we exclude cells that are putative doublets or clustered by technical artifacts, which is a much smaller sample size than our inDrop dataset.

  • The data from Okaty et al. (2015) will make for a better “reference”, although most of the data was obtained using bulk RNA-seq of the sorted/hand-picked cells, with a much smaller single-cell dataset.

However, the results of clustering the 5-HT neurons in the Zeisel et al. (2018) dataset already show some agreement with the findings we have made from our scRNA-seq data – the R1-DR neurons in the Zeisel et al. (2018) dataset also separate into 2 distinct groups that each have additional heterogeneity within them, which appears to be similar to the separation that we see between the 5-HT-I/II and 5-HT-III/IV groups.

Few cells in this dataset were found to be Met+, and appear in both the R1 (medial DRN) cluster and the MRN (R1/R2) cluster. These cells are probably similar to the 5-HT-V subtype, although it is difficult to determine if any of these Met+ cells are actually in the DRN baed on the transcriptomic data alone.

Session Information

Machine specifications:

  • Mac Pro (Late 2013)
  • macOS High Sierra 10.13.4
  • 3.7 GHz Quad-Core Intel Xeon E5
  • 64 GB 1866 MHz DDR3
  • Java version “1.8.0_172”
devtools::session_info()
Session info -----------------------------------------------------------------------------------------
 setting  value                       
 version  R version 3.4.4 (2018-03-15)
 system   x86_64, darwin15.6.0        
 ui       RStudio (1.1.447)           
 language (EN)                        
 collate  en_US.UTF-8                 
 tz       America/New_York            
 date     2019-05-07                  
Packages ---------------------------------------------------------------------------------------------
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---
title: "DRN Cell Types Project: Comparison with Linnarsson Dataset - Unsupervised Clustering"
output: 
  html_notebook:
    toc: yes
    toc_depth: 3
    toc_float: yes
author: "Kee Wui Huang, Sabatini Lab (Harvard Medical School)"
date: "`r format(Sys.time(), '%d %B, %Y')`"
---

# Notebook Overiew
This notebook is a part of a series that documents the review analysis for the DRN Cell Types project.

This notebook contains the code used to perform unsupervised clustering on the 5-HT neurons in the scRNA-seq dataset from Zeisel et al. (2018). Clusters were then filtered and identified using similar methods used in the analysis of our DRN inDrop scRNA-seq dataset.


# Analysis Workflow

## 1. Initialize

### 1.1 Load libraries
```{r}
library(devtools)
library(useful)
library(dplyr)
library(ggplot2)
library(reticulate)
library(Seurat)
library(stringr)
library(Matrix)
library(parallel)
library(ape)
```

### 1.2 Load data
```{r}
wd <- "/Volumes/LaCie/Dropbox/Sabatini Lab/DRN Cell Types Project/DRN Cell Types Manuscript/Revisions (1)/RNA-seq/"
dRaphe.neurons <- readRDS(file.path(wd, "DRN_inDrop_neurons.rds"))
zeisel.ob <- readRDS(file.path(wd, "zeisel_Seurat_cholinergic_monoaminergic_peptidergic.rds"))
dim(dRaphe.neurons@data)
dim(zeisel.ob@data)
```

```{r}
table(dRaphe.neurons@ident)
table(zeisel.ob@ident)
```

### 1.3 Check and correct gene name differences
```{r}
huang.geneNames <- rownames(dRaphe.neurons@data)
zeisel.geneNames <- rownames(zeisel.ob@data)
length(huang.geneNames)
length(zeisel.geneNames)
length(intersect(huang.geneNames, zeisel.geneNames))
```

```{r}
zeisel.geneNames.fix <- str_replace(string = zeisel.geneNames,
                                    pattern = "-",
                                    replacement = ".")
length(intersect(huang.geneNames, zeisel.geneNames.fix))
```

```{r}
is.element("mt.Co2", huang.geneNames) & is.element("mt.Co2", zeisel.geneNames.fix)
```

```{r}
head(setdiff(huang.geneNames, zeisel.geneNames.fix), 10)
```

```{r}
head(setdiff(zeisel.geneNames.fix, huang.geneNames), 10)
```

Fixing them now probably matters more for trying to merge and cluster the cells from both datasets together.

We will find the intersection after separating out the 5-HT neurons from each dataset.


## 2. Average the Zeisel data by cluster

### 2.1 Subset the 5-HT neurons
```{r}
zeisel.ob.SER <- SubsetData(object = zeisel.ob,
                            ident.use = c("HBSER1",
                                          "HBSER2",
                                          "HBSER3",
                                          "HBSER4",
                                          "HBSER5"),
                            subset.raw = TRUE)
dRaphe.neurons.5HT <- SubsetData(object = dRaphe.neurons,
                            ident.use = c("5-HT-I",
                                          "5-HT-II",
                                          "5-HT-III",
                                          "5-HT-IV",
                                          "5-HT-V"),
                            subset.raw = TRUE)
dim(dRaphe.neurons.5HT@data)
dim(zeisel.ob.SER@data)
```

```{r}
summary(dRaphe.neurons.5HT@meta.data$nUMI)
summary(zeisel.ob.SER@meta.data$nUMI)
```

```{r}
summary(dRaphe.neurons.5HT@meta.data$nGene)
summary(zeisel.ob.SER@meta.data$nGene)
```

```{r}
dRaphe.neurons.5HT <- SubsetData(object = dRaphe.neurons,
                                 ident.use = c("5-HT-I",
                                               "5-HT-II",
                                               "5-HT-III",
                                               "5-HT-IV",
                                               "5-HT-V"),
                                 subset.raw = TRUE)
dim(dRaphe.neurons.5HT@data)
```

### 2.2 Normalize the data
```{r}
zeisel.ob.SER <- NormalizeData(object = zeisel.ob.SER,
                               normalization.method = "LogNormalize",
                               scale.factor = 10000,
                               display.progress = FALSE)
dRaphe.neurons.5HT <- NormalizeData(object = dRaphe.neurons.5HT,
                                    normalization.method = "LogNormalize",
                                    scale.factor = 10000,
                                    display.progress = FALSE)
```

### 2.3 Find the expressed genes in both subsets of 5-HT neurons
```{r}
huang.5HT.genes <- rownames(dRaphe.neurons.5HT@raw.data[(rowSums(as.matrix(dRaphe.neurons.5HT@raw.data))>0),])
zeisel.5HT.genes <- rownames(zeisel.ob.SER@raw.data[(rowSums(as.matrix(zeisel.ob.SER@raw.data))>0),])
genes.common <- intersect(huang.5HT.genes, zeisel.5HT.genes)
length(genes.common)
is.element("Tph2", genes.common)
is.element("En1", genes.common)
is.element("Fev", genes.common)
is.element("Pdyn", genes.common)
is.element("Trh", genes.common)
is.element("Cbln2", genes.common)
is.element("Met", genes.common)
```

### 2.4 Calculate cluster averages
```{r}
zeisel.cluster.SER.avg <- AverageExpression(object = zeisel.ob.SER,
                                            genes.use = genes.common,
                                            return.seurat = TRUE,
                                            show.progress = FALSE)
dim(zeisel.cluster.SER.avg@data)
```

```{r}
huang.5HT.subtype.avg <- AverageExpression(object = dRaphe.neurons.5HT,
                                           genes.use = genes.common,
                                           return.seurat = TRUE,
                                           show.progress = FALSE)
dim(huang.5HT.subtype.avg@data)
```

```{r}
dRaphe.neurons <- SetIdent(object = dRaphe.neurons,
                           cells.use = WhichCells(object = dRaphe.neurons,
                                                  ident = c("5-HT-I",
                                                            "5-HT-II",
                                                            "5-HT-III",
                                                            "5-HT-IV",
                                                            "5-HT-V")),
                               ident.use = "5-HT")
dRaphe.neurons <- SetIdent(object = dRaphe.neurons,
                           cells.use = WhichCells(object = dRaphe.neurons,
                                                  ident = c("DA-I",
                                                            "DA-II",
                                                            "DA-III")),
                               ident.use = "DA")
dRaphe.neurons <- SetIdent(object = dRaphe.neurons,
                           cells.use = WhichCells(object = dRaphe.neurons,
                                                  ident = c("GABA-I",
                                                            "GABA-II",
                                                            "GABA-III")),
                               ident.use = "GABA")
dRaphe.neurons <- SetIdent(object = dRaphe.neurons,
                           cells.use = WhichCells(object = dRaphe.neurons,
                                                  ident = c("Glu-I",
                                                            "Glu-II",
                                                            "Glu-III",
                                                            "Glu-IV",
                                                            "Glu-V")),
                               ident.use = "Glu")
dRaphe.neurons.avg <- AverageExpression(object = dRaphe.neurons,
                                       genes.use = genes.common,
                                       return.seurat = TRUE,
                                       show.progress = FALSE)
dim(dRaphe.neurons.avg@data)
```

Check correlation coefficients:
```{r}
cor(x = as.vector(dRaphe.neurons.avg@data[,"5-HT"]), 
    y = as.vector(zeisel.cluster.SER.avg@data[,"HBSER1"]),
    method = "pearson")
cor(x = as.vector(dRaphe.neurons.avg@data[,"5-HT"]), 
    y = as.vector(zeisel.cluster.SER.avg@data[,"HBSER2"]),
    method = "pearson")
cor(x = as.vector(dRaphe.neurons.avg@data[,"5-HT"]), 
    y = as.vector(zeisel.cluster.SER.avg@data[,"HBSER3"]),
    method = "pearson")
cor(x = as.vector(dRaphe.neurons.avg@data[,"5-HT"]), 
    y = as.vector(zeisel.cluster.SER.avg@data[,"HBSER4"]),
    method = "pearson")
cor(x = as.vector(dRaphe.neurons.avg@data[,"5-HT"]), 
    y = as.vector(zeisel.cluster.SER.avg@data[,"HBSER5"]),
    method = "pearson")
```

```{r}
cor(x = as.vector(zeisel.cluster.SER.avg@data[,"HBSER1"]), 
    y = as.vector(zeisel.cluster.SER.avg@data[,"HBSER2"]),
    method = "pearson")
cor(x = as.vector(zeisel.cluster.SER.avg@data[,"HBSER1"]), 
    y = as.vector(zeisel.cluster.SER.avg@data[,"HBSER3"]),
    method = "pearson")
cor(x = as.vector(zeisel.cluster.SER.avg@data[,"HBSER1"]), 
    y = as.vector(zeisel.cluster.SER.avg@data[,"HBSER4"]),
    method = "pearson")
cor(x = as.vector(zeisel.cluster.SER.avg@data[,"HBSER1"]), 
    y = as.vector(zeisel.cluster.SER.avg@data[,"HBSER5"]),
    method = "pearson")
```
Coefficients are all fairly high, probably because there are a lot of genes in common between all of the different 5-HT neuron clusters. Clustering the Zeisel dataset cells will probably be more informative.


## 3. Cluster 5-HT neurons in Zeisel dataset separately

### 3.1 Scale data
```{r}
zeisel.ob.SER <- ScaleData(object = zeisel.ob.SER,
                           vars.to.regress = c("nUMI", "percent.mito"),
                           model.use = "linear",
                           do.scale = TRUE,
                           scale.max = 10,
                           do.center = TRUE,
                           do.par = TRUE,
                           num.cores = 4,
                           display.progress = FALSE)
```

### 3.2 Find variable genes
```{r, fig.width=10}
zeisel.ob.SER <- FindVariableGenes(object = zeisel.ob.SER,
                                   mean.function = ExpMean,
                                   dispersion.function = LogVMR,
                                   x.low.cutoff = 0.075,
                                   x.high.cutoff = 4,
                                   y.cutoff = 0.5,
                                   num.bin = 100)
```

```{r}
length(zeisel.ob.SER@var.genes)
```

### 3.3 PCA
```{r}
zeisel.ob.SER <- RunPCA(object = zeisel.ob.SER,
                        pcs.compute = 30,
                        pcs.print = NA,
                        weight.by.var = FALSE)
```

```{r, fig.width=16}
DimHeatmap(object = zeisel.ob.SER,
           reduction.type = "pca",
           cells.use = length(zeisel.ob.SER@cell.names),
           dim.use = 1:9,
           do.balanced = TRUE)
DimHeatmap(object = zeisel.ob.SER,
           reduction.type = "pca",
           cells.use = length(zeisel.ob.SER@cell.names),
           dim.use = 10:18,
           do.balanced = TRUE)
```

```{r, fig.width=16}
PCElbowPlot(object= zeisel.ob.SER, num.pc = 30)
```

```{r}
zeisel.ob.SER <- JackStraw(object = zeisel.ob.SER,
                           num.pc = 30,
                           do.par = TRUE,
                           num.cores = 4,
                           display.progress = FALSE)
```

```{r, fig.width=16}
JackStrawPlot(object = zeisel.ob.SER,
              PCs = 1:30)
```

### 3.4 DR (UMAP)
```{r}
zeisel.ob.SER <- RunUMAP(object = zeisel.ob.SER,
                         reduction.use = "pca",
                         dims.use = 1:10,
                         n_neighbors = 30L,
                         min_dist = 0.5,
                         metric = "correlation")
```

```{r, fig.width=16}
DimPlot(object = zeisel.ob.SER,
        reduction.use = "umap",
        no.legend = TRUE,
        do.label = TRUE,
        pt.size = 3,
        label.size = 10)
```


### 3.5 Check marker expression
```{r, fig.width=16, fig.height=10}
VlnPlot(object = zeisel.ob.SER,
        features.plot = c("Slc6a4",
                          "Tph2",
                          "Fev",
                          "Htr1a",
                          "Slc22a3",
                          "Maob"),
        nCol = 1,
        point.size.use = 0,
        size.x.use = 5,
        size.y.use = 5,
        x.lab.rot = TRUE)
VlnPlot(object = zeisel.ob.SER,
        features.plot = c("En1",
                          "En2",
                          "Pou3f1",
                          "Met",
                          "Plxnb1",
                          "Pnoc",
                          "Htr2a"),
        nCol = 1,
        point.size.use = 0,
        size.x.use = 5,
        size.y.use = 5,
        x.lab.rot = TRUE)
```

The R2-enriched gene *Hoxa2* was not detected.

```{r, fig.width=16}
FeaturePlot(object = zeisel.ob.SER,
            reduction.use = "umap",
            features.plot = c("nUMI", "Snap25", "En1",
                              "Gabre", "Sema5a", "Kit",
                              "Hrh3", "Slc17a8", "Arhgap36"),
            cols.use = c("gray", "red"))
FeaturePlot(object = zeisel.ob.SER,
            reduction.use = "umap",
            features.plot = c("Slc6a4", "Tph2", "Fev",
                              "En1", "En2", "Pnoc",
                              "Slc17a8", "Pdyn", "Prkcq"),
            cols.use = c("gray", "red"))
FeaturePlot(object = zeisel.ob.SER,
            reduction.use = "umap",
            features.plot = c("Trh", "Asb4", "Cbln2",
                              "Gad1", "Gad2", "Tac1",
                              "Nr2f2", "Cdh4", "Oxtr"),
            cols.use = c("gray", "red"))
```


### 3.6 Clustering

```{r}
resolution.vector <- c(0.5, 0.6, 0.8, 1, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8)
nClusters <- rep(0, length(resolution.vector))
oobe.max <- rep(0, length(resolution.vector))
oobe.min <- rep(0, length(resolution.vector))
oobe.mean <- rep(0, length(resolution.vector))

for (i in 1:length(resolution.vector)) {
  zeisel.ob.SER <- FindClusters(object = zeisel.ob.SER,
                              reduction.type = "pca",
                              dims.use = 1:10,
                              resolution = resolution.vector[i],
                              algorithm = 3,
                              n.iter = 10,
                              save.SNN = TRUE,
                              force.recalc = TRUE,
                              print.output = FALSE)
  
  # Average expression and build cluster tree
  zeisel.ob.SER.averages <- AverageExpression(object = zeisel.ob.SER, 
                                               return.seurat = TRUE,
                                               show.progress = FALSE)
  d <- dist(x = t(zeisel.ob.SER.averages@scale.data), 
            method = "euclidean")
  hc <- hclust(d, method = "ward.D2")
  oldIDs <- hc$labels[hc$order]
  newIDs <- c(1:length(levels(zeisel.ob.SER@ident)))
  zeisel.ob.SER@ident <- plyr::mapvalues(x = zeisel.ob.SER@ident,
                                          from = oldIDs,
                                          to = newIDs)
  level.order <- order(as.numeric(levels(zeisel.ob.SER@ident)))
  zeisel.ob.SER@ident <- factor(x = zeisel.ob.SER@ident,
                                 levels = levels(zeisel.ob.SER@ident)[level.order],
                                 ordered = TRUE)
  # Rebuild and store the cluster tree on the new identities
  zeisel.ob.SER.averages <- AverageExpression(object = zeisel.ob.SER, 
                                               return.seurat = TRUE,
                                               show.progress = FALSE)
  d <- dist(x = t(zeisel.ob.SER.averages@scale.data), 
            method = "euclidean")
  hc <- hclust(d, method = "ward.D2")
  zeisel.ob.SER@cluster.tree[[1]] <- as.phylo(hc)
  nodes.oobe <- AssessNodes(object = zeisel.ob.SER, 
                            genes.training = zeisel.ob.SER@var.genes)
  
  nClusters[i] <- length(levels(zeisel.ob.SER@ident))
  oobe.max[i] <- max(as.vector(nodes.oobe$oobe))
  oobe.min[i] <- min(as.vector(nodes.oobe$oobe))
  oobe.mean[i] <- mean(as.vector(nodes.oobe$oobe))
}
```

```{r, fig.width=16}
clusteringResults <- data.frame(resolution.vector,
                                nClusters,
                                oobe.min,
                                oobe.max,
                                oobe.mean)
plot.nClusters <- ggplot(data = clusteringResults,
                         aes(x = resolution.vector,
                             y = nClusters)) + geom_line() + geom_point()
plot.oobe.min <- ggplot(data = clusteringResults,
                         aes(x = resolution.vector,
                             y = oobe.min)) + geom_line() + geom_point()
plot.oobe.max <- ggplot(data = clusteringResults,
                         aes(x = resolution.vector,
                             y = oobe.max)) + geom_line() + geom_point()
plot.oobe.mean <- ggplot(data = clusteringResults,
                         aes(x = resolution.vector,
                             y = oobe.mean)) + geom_line() + geom_point()
plot.list <- list(plot.nClusters,
                  plot.oobe.min,
                  plot.oobe.max,
                  plot.oobe.mean)
plot_grid(plotlist = plot.list, ncol = 2)
```

```{r, fig.width=16}
zeisel.ob.SER <- FindClusters(object = zeisel.ob.SER,
                              reduction.type = "pca",
                              dims.use = 1:10,
                              resolution = 1.5,
                              algorithm = 3,
                              n.iter = 10,
                              save.SNN = TRUE,
                              force.recalc = TRUE,
                              print.output = FALSE)
# Average expression and build cluster tree
zeisel.ob.SER.averages <- AverageExpression(object = zeisel.ob.SER, 
                                             return.seurat = TRUE,
                                             show.progress = FALSE)
d <- dist(x = t(zeisel.ob.SER.averages@scale.data), 
          method = "euclidean")
hc <- hclust(d, method = "ward.D2")
oldIDs <- hc$labels[hc$order]
newIDs <- c(1:length(levels(zeisel.ob.SER@ident)))
zeisel.ob.SER@ident <- plyr::mapvalues(x = zeisel.ob.SER@ident,
                                        from = oldIDs,
                                        to = newIDs)
level.order <- order(as.numeric(levels(zeisel.ob.SER@ident)))
zeisel.ob.SER@ident <- factor(x = zeisel.ob.SER@ident,
                               levels = levels(zeisel.ob.SER@ident)[level.order],
                               ordered = TRUE)
# Rebuild and store the cluster tree on the new identities
zeisel.ob.SER.averages <- AverageExpression(object = zeisel.ob.SER, 
                                             return.seurat = TRUE,
                                             show.progress = FALSE)
d <- dist(x = t(zeisel.ob.SER.averages@scale.data), 
          method = "euclidean")
hc <- hclust(d, method = "ward.D2")
zeisel.ob.SER@cluster.tree[[1]] <- as.phylo(hc)
```

```{r, fig.width=16}
PlotClusterTree(zeisel.ob.SER)
```

```{r, fig.width=16}
p1 <- DimPlot(object = zeisel.ob.SER,
              reduction.use = "umap",
              no.legend = TRUE,
              do.label = TRUE,
              pt.size = 3,
              label.size = 10,
              do.return = TRUE)
p2 <- DimPlot(object = zeisel.ob.SER,
              reduction.use = "umap",
              no.legend = TRUE,
              do.label = TRUE,
              pt.size = 3,
              label.size = 10,
              group.by = "zeisel.clusterNames",
              do.return = TRUE)
plot_grid(p1, p2)
```

```{r}
nodes.oobe <- AssessNodes(zeisel.ob.SER, genes.training = zeisel.ob.SER@var.genes)
nodes.oobe <- nodes.oobe[order(nodes.oobe$oobe, decreasing = TRUE),]
nodes.oobe
```

```{r}
nodes.oobe
```


```{r, fig.width=16, fig.height=10}
VlnPlot(object = zeisel.ob.SER,
        features.plot = c("nUMI",
                          "Slc6a4",
                          "Tph2",
                          "Fev",
                          "Htr1a",
                          "Slc22a3",
                          "Maob"),
        nCol = 1,
        point.size.use = 0,
        size.x.use = 5,
        size.y.use = 5,
        x.lab.rot = TRUE)
VlnPlot(object = zeisel.ob.SER,
        features.plot = c("En1",
                          "En2",
                          "Pou3f1",
                          "Met",
                          "Plxnb1",
                          "Pnoc",
                          "Htr2a"),
        nCol = 1,
        point.size.use = 0,
        size.x.use = 5,
        size.y.use = 5,
        x.lab.rot = TRUE)
```

```{r}
table(zeisel.ob.SER@ident)
```

### 3.7 Find genes enriched in each cluster
```{r}
markers.cluster <- FindAllMarkers(object = zeisel.ob.SER,
                                  test.use = "MAST",
                                  only.pos = TRUE)
markers.node <- FindAllMarkersNode(object = zeisel.ob.SER,
                                   test.use = "MAST",
                                   only.pos = FALSE)
```

```{r}
head(markers.cluster[markers.cluster$cluster==1,], 100)
```

Cluster 1 is highly enriched in ribosomal genes, and the separation seems likely to be an artifact. Should also check DE genes at node #10.


```{r}
head(markers.cluster[markers.cluster$cluster==2,], 50)
```

Cluster 2 is likely a part of R1DR (dorsal DRN).


```{r}
head(markers.cluster[markers.cluster$cluster==3,], 50)
```

Cluster 3 is likely a part of R1DR (ventral DRN).

```{r}
head(markers.cluster[markers.cluster$cluster==4,], 50)
```

```{r, fig.width=16}
FeaturePlot(object = zeisel.ob.SER,
            reduction.use = "umap",
            features.plot = c("Gpr101", "Trh", "Sst",
                              "Penk", "Trhr", "Reln",
                              "Ndnf", "Mc4r", "Pou3f2"),
            cols.use = c("gray", "red"))
```

Cluster 4 gene expression matches the R6P cluster in Okaty et al. (2015).


```{r}
head(markers.cluster[markers.cluster$cluster==5,], 50)
```

```{r, fig.width=16}
FeaturePlot(object = zeisel.ob.SER,
            reduction.use = "umap",
            features.plot = c("Mal", "Cryab", "Fa2h",
                              "Kcnj10", "Enpp2", "Olig1",
                              "Mog", "Kcna1", "Nkx6-2"),
            cols.use = c("gray", "red"))
```
Cells in cluster 5 look a lot like putative doublets, given the high expression of genes typically found in glial cells (particularly oligodendrocyte genes).


```{r}
head(markers.cluster[markers.cluster$cluster==6,], 50)
```

Cluster 6 is likely to be R1MR (or R1DR/R2).


```{r}
head(markers.cluster[markers.cluster$cluster==7,], 50)
```

Cluster 7 cells are likely to be R5 cells.


```{r, fig.width=16}
FeaturePlot(object = zeisel.ob.SER,
            reduction.use = "umap",
            features.plot = c("Pgr15l", "Tac1", "Shox2",
                              "Chodl", "Nkx6-1", "Meis2",
                              "Islr2", "Arhgap36", "Trh"),
            cols.use = c("gray", "red"))
```

```{r}
head(markers.cluster[markers.cluster$cluster==8,], 50)
```

```{r, fig.width=16}
FeaturePlot(object = zeisel.ob.SER,
            reduction.use = "umap",
            features.plot = c("Rorb", "Pcdh19", "Nr2f2",
                              "Tcf4", "Foxp1", "Esr2",
                              "Kit", "Sema5a", "Cntn3"),
            cols.use = c("gray", "red"))
```

Cluster 8 is either R1MR or R2, likely R2 (MRN cells, either way).

Check expression of R3-enriched genes:
```{r, fig.width=16}
FeaturePlot(object = zeisel.ob.SER,
            reduction.use = "umap",
            features.plot = c("Egr2", "Samd9l", "Plxnb1",
                              "Pdlim5", "Pcdh18", "Zfp560",
                              "Trpc5", "Sstr1", "Cdh9"),
            cols.use = c("gray", "red"))
```

It's possible that there aren't enough R3 cells in this dataset to detect.

Check DRN 5-HT subtypes:
```{r, fig.width=16}
FeaturePlot(object = zeisel.ob.SER,
            reduction.use = "umap",
            features.plot = c("En1", "Adcy2", "Pdyn",
                              "Slc17a8", "Cbln2", "Arhgap36",
                              "Prkcq", "Gad1", "Trh"),
            cols.use = c("gray", "red"))
FeaturePlot(object = zeisel.ob.SER,
            reduction.use = "umap",
            features.plot = c("Hcrtr1", "Fbxo2", "Sorcs1",
                              "Crhr2", "Rprm", "Asb4",
                              "En2", "Trpc3", "Met"),
            cols.use = c("gray", "red"))
```

### 3.8 Check DE genes by nodes (left vs. right)
```{r}
head(markers.node[markers.node$cluster==9,], 50)
```

```{r}
head(markers.node[markers.node$cluster==10,], 50)
```

```{r, fig.width=16}
FeaturePlot(object = zeisel.ob.SER,
            reduction.use = "umap",
            features.plot = c("Gabre", "Sema5a", "Gabrq",
                              "Tac1", "Pou2f2", "Hrh3",
                              "En1", "Amigo2", "Kit"),
            cols.use = c("gray", "red"))
```

Cluster 1 seems to be a collection of cells from different clusters (mixed expression of genes in R1/R2/R3/R5/R6 cells) that share "high expression"" on ribosomal genes.


```{r}
head(markers.node[markers.node$cluster==14,], 50)
```

```{r, fig.width=16}
FeaturePlot(object = zeisel.ob.SER,
            reduction.use = "umap",
            features.plot = c("Slc17a8", "Slit2", "Pcdh11x",
                              "Nos1", "Npy2r", "Crhr2",
                              "Pcdh15", "Maf", "Ntng1"),
            cols.use = c("gray", "red"))
FeaturePlot(object = zeisel.ob.SER,
            reduction.use = "umap",
            features.plot = c("Hs3st4", "Asb4", "Prph",
                              "Arhgap36", "Gad1", "Tmem176b",
                              "Sdk2", "Hcrtr1", "Esm1"),
            cols.use = c("gray", "red"))
```

Node 14 is probably a split between dorsal and ventral DRN.


```{r}
head(markers.node[markers.node$cluster==11,], 50)
```

```{r}
head(markers.node[markers.node$cluster==12,], 50)
```

```{r}
head(markers.node[markers.node$cluster==13,], 50)
```

```{r}
head(markers.node[markers.node$cluster==15,], 50)
```

### 3.9 Filter clusters and re-cluster
```{r}
zeisel.ob.SER <- SubsetData(object = zeisel.ob.SER,
                            ident.remove = c(1, 5),
                            subset.raw = TRUE)
```

```{r}
resolution.vector <- c(0.5, 0.6, 0.8, 1, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8)
nClusters <- rep(0, length(resolution.vector))
oobe.max <- rep(0, length(resolution.vector))
oobe.min <- rep(0, length(resolution.vector))
oobe.mean <- rep(0, length(resolution.vector))

for (i in 1:length(resolution.vector)) {
  zeisel.ob.SER <- FindClusters(object = zeisel.ob.SER,
                              reduction.type = "pca",
                              dims.use = 1:10,
                              resolution = resolution.vector[i],
                              algorithm = 3,
                              n.iter = 10,
                              save.SNN = TRUE,
                              force.recalc = TRUE,
                              print.output = FALSE)
  
  # Average expression and build cluster tree
  zeisel.ob.SER.averages <- AverageExpression(object = zeisel.ob.SER, 
                                               return.seurat = TRUE,
                                               show.progress = FALSE)
  d <- dist(x = t(zeisel.ob.SER.averages@scale.data), 
            method = "euclidean")
  hc <- hclust(d, method = "ward.D2")
  oldIDs <- hc$labels[hc$order]
  newIDs <- c(1:length(levels(zeisel.ob.SER@ident)))
  zeisel.ob.SER@ident <- plyr::mapvalues(x = zeisel.ob.SER@ident,
                                          from = oldIDs,
                                          to = newIDs)
  level.order <- order(as.numeric(levels(zeisel.ob.SER@ident)))
  zeisel.ob.SER@ident <- factor(x = zeisel.ob.SER@ident,
                                 levels = levels(zeisel.ob.SER@ident)[level.order],
                                 ordered = TRUE)
  # Rebuild and store the cluster tree on the new identities
  zeisel.ob.SER.averages <- AverageExpression(object = zeisel.ob.SER, 
                                               return.seurat = TRUE,
                                               show.progress = FALSE)
  d <- dist(x = t(zeisel.ob.SER.averages@scale.data), 
            method = "euclidean")
  hc <- hclust(d, method = "ward.D2")
  zeisel.ob.SER@cluster.tree[[1]] <- as.phylo(hc)
  nodes.oobe <- AssessNodes(object = zeisel.ob.SER, 
                            genes.training = zeisel.ob.SER@var.genes)
  
  nClusters[i] <- length(levels(zeisel.ob.SER@ident))
  oobe.max[i] <- max(as.vector(nodes.oobe$oobe))
  oobe.min[i] <- min(as.vector(nodes.oobe$oobe))
  oobe.mean[i] <- mean(as.vector(nodes.oobe$oobe))
}
```

```{r, fig.width=16}
clusteringResults <- data.frame(resolution.vector,
                                nClusters,
                                oobe.min,
                                oobe.max,
                                oobe.mean)
plot.nClusters <- ggplot(data = clusteringResults,
                         aes(x = resolution.vector,
                             y = nClusters)) + geom_line() + geom_point()
plot.oobe.min <- ggplot(data = clusteringResults,
                         aes(x = resolution.vector,
                             y = oobe.min)) + geom_line() + geom_point()
plot.oobe.max <- ggplot(data = clusteringResults,
                         aes(x = resolution.vector,
                             y = oobe.max)) + geom_line() + geom_point()
plot.oobe.mean <- ggplot(data = clusteringResults,
                         aes(x = resolution.vector,
                             y = oobe.mean)) + geom_line() + geom_point()
plot.list <- list(plot.nClusters,
                  plot.oobe.min,
                  plot.oobe.max,
                  plot.oobe.mean)
plot_grid(plotlist = plot.list, ncol = 2)
```


```{r}
zeisel.ob.SER <- FindClusters(object = zeisel.ob.SER,
                              reduction.type = "pca",
                              dims.use = 1:10,
                              resolution = 1.1,
                              algorithm = 3,
                              n.iter = 10,
                              save.SNN = TRUE,
                              force.recalc = TRUE,
                              print.output = FALSE)
# Average expression and build cluster tree
zeisel.ob.SER.averages <- AverageExpression(object = zeisel.ob.SER, 
                                             return.seurat = TRUE,
                                             show.progress = FALSE)
d <- dist(x = t(zeisel.ob.SER.averages@scale.data), 
          method = "euclidean")
hc <- hclust(d, method = "ward.D2")
oldIDs <- hc$labels[hc$order]
newIDs <- c(1:length(levels(zeisel.ob.SER@ident)))
zeisel.ob.SER@ident <- plyr::mapvalues(x = zeisel.ob.SER@ident,
                                        from = oldIDs,
                                        to = newIDs)
level.order <- order(as.numeric(levels(zeisel.ob.SER@ident)))
zeisel.ob.SER@ident <- factor(x = zeisel.ob.SER@ident,
                               levels = levels(zeisel.ob.SER@ident)[level.order],
                               ordered = TRUE)
# Rebuild and store the cluster tree on the new identities
zeisel.ob.SER.averages <- AverageExpression(object = zeisel.ob.SER, 
                                             return.seurat = TRUE,
                                             show.progress = FALSE)
d <- dist(x = t(zeisel.ob.SER.averages@scale.data), 
          method = "euclidean")
hc <- hclust(d, method = "ward.D2")
zeisel.ob.SER@cluster.tree[[1]] <- as.phylo(hc)
```

```{r, fig.width=16}
PlotClusterTree(zeisel.ob.SER)
```

```{r}
zeisel.ob.SER <- RunUMAP(object = zeisel.ob.SER,
                         reduction.use = "pca",
                         dims.use = 1:10,
                         n_neighbors = 30L,
                         min_dist = 0.5,
                         metric = "correlation")
```

```{r, fig.width=16}
p1 <- DimPlot(object = zeisel.ob.SER,
              reduction.use = "umap",
              no.legend = TRUE,
              do.label = TRUE,
              pt.size = 3,
              label.size = 10,
              do.return = TRUE)
p2 <- DimPlot(object = zeisel.ob.SER,
              reduction.use = "umap",
              no.legend = TRUE,
              do.label = TRUE,
              pt.size = 3,
              label.size = 10,
              group.by = "zeisel.clusterNames",
              do.return = TRUE)
plot_grid(p1, p2)
```

```{r, fig.width=16}
p1
```

```{r, fig.width=16}
# QC, 5-HT markers
FeaturePlot(object = zeisel.ob.SER,
            reduction.use = "umap",
            features.plot = c("nUMI", "percent.mito", "Ddc",
                              "Slc18a2", "Tph2", "Slc6a4",
                              "Maob", "Slc22a3", "Qdpr"),
            cols.use = c("gray", "red"))
# Zeisel et al. (2018), HBSER4 vs. HBSER5
FeaturePlot(object = zeisel.ob.SER,
            reduction.use = "umap",
            features.plot = c("Lat2", "Gpr101", "Penk",
                              "Trh", "Sst", "Calcr",
                              "Shox2", "Chodl", "Prkcq"),
            cols.use = c("gray", "red"))
# Zeisel et al. (2018), HBSER1 vs. HBSER3
FeaturePlot(object = zeisel.ob.SER,
            reduction.use = "umap",
            features.plot = c("Satb2", "Nbl1", "Satb2",
                              "Lrrk1", "Wfdc12", "Kit",
                              "Kcns1", "Col25a1", "Il1r1"),
            cols.use = c("gray", "red"))
# Zeisel et al. (2018), HBSER2
FeaturePlot(object = zeisel.ob.SER,
            reduction.use = "umap",
            features.plot = c("Sostdc1", "Rasgrp1", "Gata3",
                              "Htr5b", "Sox1", "Pcsk5",
                              "Hs6st2", "Mbp", "Nos1"),
            cols.use = c("gray", "red"))
# R1-DR
FeaturePlot(object = zeisel.ob.SER,
            reduction.use = "umap",
            features.plot = c("En1", "En2", "Satb2",
                              "Npy2r", "Pax5", "Rspo3",
                              "Foxa1", "Pcsk6", "Prph"),
            cols.use = c("gray", "red"))
# R1-DR, dorsal/lateral vs. ventral/midline
FeaturePlot(object = zeisel.ob.SER,
            reduction.use = "umap",
            features.plot = c("Asb4", "Gad1", "Prkcq",
                              "Trh", "Hcrtr1", "Pdyn",
                              "Rprm", "Slc17a8", "Cbln2"),
            cols.use = c("gray", "red"))
# R1-MR
FeaturePlot(object = zeisel.ob.SER,
            reduction.use = "umap",
            features.plot = c("Sox1", "Rorb", "Chrm2",
                              "Trpc5", "Esr2", "Epha4",
                              "Ppp1r17", "Pkd2l1", "Col19a1"),
            cols.use = c("gray", "red"))
# R2
FeaturePlot(object = zeisel.ob.SER,
            reduction.use = "umap",
            features.plot = c("Rorb", "Pcdh19", "Nr2f2",
                              "Tcf4", "Foxp1", "Met",
                              "Kit", "Sema5a", "Cntn3"),
            cols.use = c("gray", "red"))
#R5
FeaturePlot(object = zeisel.ob.SER,
            reduction.use = "umap",
            features.plot = c("Chodl", "Shox2", "Meis2",
                              "Tac1", "Nkx6-1", "Grm4",
                              "Pnoc", "Perp", "Islr2"),
            cols.use = c("gray", "red"))
# R6P
FeaturePlot(object = zeisel.ob.SER,
            reduction.use = "umap",
            features.plot = c("Gpr101", "Sst", "Penk",
                              "Trhr", "Reln", "Ndnf",
                              "Mc4r", "Pou3f2", "Trh"),
            cols.use = c("gray", "red"))
```

There appear to be additional subsets within clusters 2 and 3. We will explore this later, and go with the larger grouping for now and assign cluster identities by rhombomeres.


```{r}
nodes.oobe <- AssessNodes(zeisel.ob.SER, genes.training = zeisel.ob.SER@var.genes)
nodes.oobe <- nodes.oobe[order(nodes.oobe$oobe, decreasing = TRUE),]
nodes.oobe
```

Find genes enriched in each cluster
```{r}
markers.cluster <- FindAllMarkers(object = zeisel.ob.SER,
                                  test.use = "MAST",
                                  only.pos = TRUE)
markers.node <- FindAllMarkersNode(object = zeisel.ob.SER,
                                   test.use = "MAST",
                                   only.pos = FALSE)
```

```{r}
head(markers.cluster[markers.cluster$cluster==1,], 50)
```

```{r}
head(markers.cluster[markers.cluster$cluster==2,], 50)
```

```{r}
head(markers.cluster[markers.cluster$cluster==3,], 50)
```

```{r}
head(markers.cluster[markers.cluster$cluster==4,], 50)
```

```{r}
head(markers.cluster[markers.cluster$cluster==5,], 50)
```

```{r}
head(markers.cluster[markers.cluster$cluster==6,], 50)
```

```{r}
head(markers.node[markers.node$cluster==7,], 50)
```

```{r}
head(markers.node[markers.node$cluster==8,], 50)
```

```{r}
head(markers.node[markers.node$cluster==9,], 50)
```

```{r}
head(markers.node[markers.node$cluster==10,], 50)
```

```{r}
head(markers.node[markers.node$cluster==11,], 50)
```

Could not find any cells/clusters corresponding to R3 in this dataset.

Cluster 2 looks very similar to clusters 5 and 6. Compare cluster 2 vs. 5 & 6:
```{r}
markers.compare <- FindMarkers(object = zeisel.ob.SER,
                               ident.1 = 2,
                               ident.2 = c(5, 6),
                               test.use = "MAST",
                               only.pos = FALSE)
```

```{r}
head(markers.compare, 50)
```

```{r, fig.width=16}
FeaturePlot(object = zeisel.ob.SER,
            reduction.use = "umap",
            features.plot = c("Eef1a1", "Rpl41", "Rps19",
                              "Rpl23a", "Rpl37a", "Rps28",
                              "Tmsb10", "mt-Co1", "Rps29"),
            cols.use = c("gray", "red"))
FeaturePlot(object = zeisel.ob.SER,
            reduction.use = "umap",
            features.plot = c("Cldn11", "Ptges3", "Elmod1",
                              "Gprasp2", "St13", "Nptn",
                              "Gmps", "Xist", "Klhl9"),
            cols.use = c("gray", "red"))
```

Check other R1-MR vs. R1-DR genes:
```{r, fig.width=16}
FeaturePlot(object = zeisel.ob.SER,
            reduction.use = "umap",
            features.plot = c("Npy1r", "Sox14", "Epha4",
                              "Col14a1", "Robo1", "Ret",
                              "Sema3e", "Cxcl14", "Klhl14"),
            cols.use = c("gray", "red"))
```

```{r, fig.width=16, fig.height=10}
VlnPlot(object = zeisel.ob.SER,
        features.plot = c("nUMI",
                          "nGene",
                          "percent.mito",
                          "Xist",
                          "Tph2"),
        nCol = 1,
        point.size.use = 0,
        size.x.use = 12,
        size.y.use = 5,
        x.lab.rot = FALSE)
```

Clusters 5 and 6 appear to be similar to cluster 2, with the separation being driven by differences that are likely to be technical or batch effects. However, there is insufficient metadata to examine this or to try and regress out these effects. It is possible that the HBSER1 cluster is separated from the other 5-HT neuron clusters in the Zeisel et al. (2018) dataset due to the same technical artifacts.

Cluster 2 appears to be a mixture of various R1 neurons (mostly R1-DR), while cluster 4 contains several MRN 5-HT neuron types that include both R1-MR and R2 neurons. It is unclear if there are any R3 neurons in this cluster, since we cannot detect cells expressing R3-enriched genes.


```{r}
table(zeisel.ob.SER@ident)
```

```{r}
zeisel.ob.SER <- StashIdent(object = zeisel.ob.SER,
                            save.name = "clusterNumbers_filtered")
```

### 3.10 Re-name clusters
```{r}
ids.current <- levels(zeisel.ob.SER@ident)
ids.rhombomere <- c("R6P (ROb/RPa)",  #1
                    "R1 (DRN)",  #2 -- probably R1-DR
                    "R5 (RMg)",  #3
                    "R1/R2 (MRN)",  #4 -- at least R1-MR and R2
                    "R1 (lateral DRN)",  #5
                    "R1 (medial DRN)")  #6
zeisel.ob.SER@ident <- plyr::mapvalues(x = zeisel.ob.SER@ident,
                                        from = ids.current,
                                        to = ids.rhombomere)
zeisel.ob.SER <- StashIdent(object = zeisel.ob.SER,
                            save.name = "clusterNames_byRhombomere")
```

```{r, fig.width=16}
p1 <- DimPlot(object = zeisel.ob.SER,
              reduction.use = "umap",
              no.legend = TRUE,
              do.label = TRUE,
              pt.size = 3,
              label.size = 6,
              do.return = TRUE)
p2 <- DimPlot(object = zeisel.ob.SER,
              reduction.use = "umap",
              no.legend = TRUE,
              do.label = TRUE,
              pt.size = 3,
              label.size = 6,
              group.by = "zeisel.clusterNames",
              do.return = TRUE)
plot_grid(p1, p2)
```

```{r}
table(zeisel.ob.SER@ident)
```

## 4. Save the object with current labels
```{r}
saveRDS(object = zeisel.ob.SER,
        file = "/Volumes/LaCie/Dropbox/Sabatini Lab/DRN Cell Types Project/DRN Cell Types Manuscript/Revisions (1)/RNA-seq/zeisel_Seurat_HBSER_clustered_filtered.rds")
```


## 5. Check distribution of `MitoRiboRatio`
```{r}
library(loomR)
```

```{r}
lfile <- connect(filename = "/Volumes/LaCie/Dropbox/Sabatini Lab/DRN Cell Types Project/DRN Cell Types Manuscript/Revisions (1)/RNA-seq/l6_r3_cholinergic_monoaminergic_and_peptidergic_neurons.loom")
lfile
```

```{r, fig.width=16}
hist(log2(lfile$col.attrs$MitoRiboRatio[]+1), breaks = 100)
```

Looks similar to the distribution of `percent.mito`. Might be worth adding this in and trying to include it as a latent variable to see if we can remove the effects driving the separation of cluster 2 from 5 and 6.

```{r}
lfile$close_all()
```


# Summary & Conclusions

The dataset from Zeisel et al. (2018) might not serve as a sutiable "reference" dataset for comparing with our inDrop scRNA-seq dataset for the following reasons:  

* 5-HT neurons in the Zeisel dataset come from multiple serotonergic nuclei, not only the dorsal raphe.  

* The clustering of 5-HT neurons by Zeisel et al. does not match the 5-HT neuron subsets described in Okaty et al. (2015), and re-clustering on the 5-HT neurons alone shows that the HBSER1-5 clusters are likely to be a mixture of 5-HT neuron types when categorized by their developmental lineage (also explaining the unusual list of DE genes in the Zeisel et al. data portal).  

* There appear to be several sources of technical variation or batch effects that drive the separation of clusters that are most similar to R1-DR cells, but there is insufficient metadata to understand the sources of these artifacts.  

* There are only 118 DRN 5-HT neurons (R1-DR) remaining if we exclude cells that are putative doublets or clustered by technical artifacts, which is a much smaller sample size than our inDrop dataset.  

* The data from Okaty et al. (2015) will make for a better "reference", although most of the data was obtained using bulk RNA-seq of the sorted/hand-picked cells, with a much smaller single-cell dataset.  

However, the results of clustering the 5-HT neurons in the Zeisel et al. (2018) dataset already show some agreement with the findings we have made from our scRNA-seq data -- the R1-DR neurons in the Zeisel et al. (2018) dataset also separate into 2 distinct groups that each have additional heterogeneity within them, which appears to be similar to the separation that we see between the 5-HT-I/II and 5-HT-III/IV groups.  

Few cells in this dataset were found to be *Met*^+^, and appear in both the R1 (medial DRN) cluster and the MRN (R1/R2) cluster. These cells are probably similar to the 5-HT-V subtype, although it is difficult to determine if any of these *Met*^+^ cells are actually in the DRN baed on the transcriptomic data alone.  


## Session Information

Machine specifications:  

* Mac Pro (Late 2013)  
* macOS High Sierra 10.13.4  
* 3.7 GHz Quad-Core Intel Xeon E5  
* 64 GB 1866 MHz DDR3  
* Java version "1.8.0_172"  

```{r}
devtools::session_info()
```